VISUALISING YOUR DATA

This page contains information on how to create visualisations of your data in the AURIN Portal

Introduction

There are a number of tools available for you to use to visualise your data, which can be accessed by clicking on the Maps, Charts and Graphs button in the Visualise pane

Clicking on this will bring up the Visualisations window, from which you can select the type of visualisation you would like to create with your data. Specific information about each of the different options available is explained below

Interactive Scatterplot User Guide

Introduction

A scatterplot is one of the easiest and more effective ways of investigating your datasets. It is a great way of ‘eyeballing’ your data to see how the variables are distributed and relate to each other. A scatterplot is not a rigourous statistical analysis, but is a very useful ‘first pass’ of data, to visualise obvious or not so obvious correlations and relationships.

The AURIN portal has two ways of creating a scatterplot from your data – the interactive scatterplot described here allows you to interact with each of the data-points and where they fit on your map, while the scatterplot described in the Chart Tools is more “bare-bones”, although it allows you easy download of the image for incorporation into documents or presentations. The interactive components of the chart are shown more fully in the outputs tab above.

Set Up

For this worked example we will investigate the relationship between some income and inequality variables across the Melbourne region.

Once you have added these datasets, you are ready to create your scatterplot – click the Inputs tab above to see how to do this

Inputs

We are now ready to create a scatterplot comparing two of our indicators

In particular, we want to look at the relationship between the poverty rate of an area (the proportion of people below a half median equivalised disposable household income poverty line) and the proportion of the population that is experiencing housing stress – the proportion of the population in the bottom two quintiles of income (the bottom 40% of income) who pay more than 30% of their income on housing – hence the 30/40 rule.

To do this click the Maps, Charts and Graphs button, click Interactive Charts and Scatter Plot. Enter your parameters as shown in the image below and click the Add and Display button

Outputs

Once you click Add and Display on the input box, a scatterplot will appear automatically in your viewer (shown below). You will also see an entry appear in your Visualise panel, with a small graph icon next to it.

If you hover over any of the dots, it will be highlighted with a red circle, with the values of the variables coming up. This becomes quite useful if you have a map of the area and the units visible as well (it can be any map that shows the units – they don’t have to be the same variables, or even from the same dataset – as long as they are mapping the same spatial units). Hovering over any of the points on the graph will show its corresponding area on the map, and vice versa, as shown in the image below

Map Visualisations: Choropleth

Introduction

A choropleth map is likely to be the most common kind of map visualisation that AURIN users will make with the data that they access.

A choropleth (from the Greek χώρο (“choro” = “area/region”) + πλήθος (“pleth” = “multitude”)) is a thematic map in which areas are shaded or patterned in proportion to the measurement of a variable being displayed on the map, such as population density or average income.

Set Up

For this worked example we will look at the distribution of mortgage costs across Tasmania.

Household Stress: Households with Mortgage Repayments Greater Than or Equal To 30% of Houshold Income %

Once you have added these datasets, you are ready to create your choropleth – click the Inputs tab above to see how to do this

Inputs

Now that you have added your datasets you are ready to create a choropleth map.

To do this, click Maps, Charts & Graphs then Map Visualisations and then Choropleth in the Visualisations pop up window (shown below). This will bring up a range of fields that need to be populated. Enter your parameters as you see them below

Select a dataset:Here you can choose which of the datasets you would like to display as a map. In this exercise we use ABS – Data by Region – Family & Community (SA2) 2011 – 2016

Select an attribute: This is the field that you want to map. If you want your map to make sense, and actually display the variable you are interested in, it is important to make sure you have selected the right attribute to map together with right classifier. For this example we choose Household Stress: Households with Mortgage Repayments Greater Than or Equal To 30% of Household Income %

Select a classifier: Here we define how we break up our range of values int he attribute. For an attribute that is numerical in format (either an integer or a decimal), the default setting for this field is Jenks (Natural Breaks), which breaks your data up into intuitive groups based on the shape of distribution of values. You can select Quantiles or Equal Intervals. If your attribute is categorical – that is, if it is a description or a word (such as a land use zone, or a name, or any kind of “string”) then the parameter will automatically set to Pre-classified. For this example we choose Jenks (Natural Breaks).

Number of Classes: This slider allows you to define the number of breaks in your data (minimum of 3, maximum of 12). The number that you choose should depend on the distribution of your values, the number of data points (areas) and the information that you are trying to portray with your data. For this example we choose 5

Select a palette type:Here you can choose the type of colour scheme for your data – Sequential, which shifts from a shade of one colour to another; Qualitative, where the colours are unique along the palette (used for Pre-classified) ; and Diverging, where colours shift to two colours from a central point along a natural spectrum. For this instance we choose Sequential.

Palette:This allows you to choose the actual colours of your palette (you can switch the ends of the palette around by clicking the Reverse Palette box at the bottom of the box. AURIN uses colours generated by Colour Brewer. For this example we select Yellow – Green – Blue (YlGnBu)

Hover Opacity:This slider allows you to define how opaque you want specific areas to be when you hover over them with a mouse, with the same values as for Default Opacity. Here we select 0.85

Stroke/Line Opacity: This slider allows you to define how opaque your polygon borders will be, with the same values as the Default and Hover Opacity. Here we select 1.0

Name: The default for this field is “[Attribute] -1” It’s a good idea to change the name of this to something that reflects the data, particularly if you plan on having multiple choropleth maps from different datasets. The name that you choose here will also be displayed in the legend automatically generated for your map. Here we use the name: Percentage of Households spending 30% or more of income on mortgage

Once you have selected your parameters click Add and Display.

Outputs

Once you click Add and Display on the input box, a map will appear automatically in your viewer which should look something like the map shown below. If you hover over any of the areas, the value for the attribute that you have mapped will show, as well as which group in the legend that specific area belongs to

Hovering over a certain class within the legend will also bring up all of the areas that belong to that specific class (shown below)

Alternatively, if you have a table open while a map visualisation is visible, hovering over an entry in the table will bring up the corresponding area on the map and vice versa. The tables and the maps do not need to be from the same dataset: if they correspond to the same geographic areas (i.e. 2016 Census SA2s from Tasmania), the interaction between the table and the map will show

Map Visualisations: Choropleth Centroid

Introduction

Centroid choropleths are another common type of visualisation tool. Although conceptually similar to classic choropleth maps, centroid choropleths differ in that the central point (“centroid”) of an area (“polygon”) on a map is represented, with varying colour and symbol size, rather than the entire area being shaded according to the variable.

The centroid is shown in proportion to the measurement of a variable being displayed on the map, such as population density or average income, or as shown below, percentage of the population involved in volunteering. A choropleth centroid map allows for two datasets to be overlaid on top of each other, one as a choropleth (such as the map of mortgage stress in the worked example above), and the other as a choropleth centroid.

Set Up

For this worked example we will look at the distribution of rental costs across Tasmania.

This worked example assumes you have knowledge of the choropleth map, explained in the previous section

Household Stress: Households with Mortgage Repayments Greater Than or Equal To 30% of Houshold Income %

Household Stress: Households With Rent Payments Greater Than or Equal To 30% of Household Income %

When you have added this dataset firstly create a choropleth of the mortgage stress variable (explained in section above)

Now you are are ready to create a choropleth centroid map – click the Inputs tab above to see how to do this.

Inputs

Now that you have added your dataset and created a choropleth map, you are ready to create a choropleth centroid map.

To do this, click Maps, Charts & Graphs then Map Visualisations and then Choropleth – Centroid in the Visualisations pop up window (shown below). This will bring up a range of fields that need to be populated. Enter your parameters as you see them below

Select a dataset:Here you can choose which of the datasets you would like to display as a map. In this exercise we use ABS – Data by Region – Family & Community (SA2) 2011 – 2016

Select an attribute: This is the field that you want to map. If you want your map to make sense, and actually display the variable you are interested in, it is important to make sure you have selected the right attribute to map together with right classifier. For this example we choose Household Stress: Households with Rent Repayments Greater Than or Equal To 30% of Household Income %

Select a classifier: Here we define how we break up our range of values int he attribute. For an attribute that is numerical in format (either an integer or a decimal), the default setting for this field is Jenks (Natural Breaks), which breaks your data up into intuitive groups based on the shape of distribution of values. You can select Quantiles or Equal Intervals. If your attribute is categorical – that is, if it is a description or a word (such as a land use zone, or a name, or any kind of “string”) then the parameter will automatically set to Pre-classified. For this example we choose Jenks (Natural Breaks).

Number of Classes: This slider allows you to define the number of breaks in your data (minimum of 3, maximum of 12). The number that you choose should depend on the distribution of your values, the number of data points (areas) and the information that you are trying to portray with your data. For this example we choose 5

Select a palette type:Here you can choose the type of colour scheme for your data – Sequential, which shifts from a shade of one colour to another; Qualitative, where the colours are unique along the palette (used for Pre-classified) ; and Diverging, where colours shift to two colours from a central point along a natural spectrum. For this instance we choose Sequential.

Palette:This allows you to choose the actual colours of your palette (you can switch the ends of the palette around by clicking the Reverse Palette box at the bottom of the box. AURIN uses colours generated by Colour Brewer. For this example we select Yellow – Green – Blue (YlGnBu)

Stroke/Line Opacity: This slider allows you to define how opaque your polygon borders will be, with the same values as the Default and Hover Opacity. Here we select 1.0

Set minimum radius:This slider allows you to scale the minimum size of the circles. It is set to a default of 1. Here we leave it at its default value of 1

Set maximum radius:This slider allows you to scale the maximum size of the circles. it is set to a default of 20. Here we leave it at its default value of 20

Select a scaling method:This option allows you to chose which methods scale your circles from the smallest to the largest circles. These options are really based on your own preferences, and there are not a huge amount of differences between the options: Mathematical, Range-Graded and Perceptual. Here we leave it at its default of Mathematical.

Name: The default for this field is “[Attribute] -1” It’s a good idea to change the name of this to something that reflects the data, particularly if you plan on having multiple choropleth maps from different datasets. The name that you choose here will also be displayed in the legend automatically generated for your map. Here we use the name: Percentage of Households spending 30% or more of income on rent

Once you have selected your parameters click Add and Display.

Outputs

Once you click Add and Display on the input box, a map will appear automatically in your viewer which should look something like the map shown below. This map shows both the choropleth and the choropleth centroid, but you will notice there are clusters of circles across towns and cities, due to the larger number of SA2s in those places.

If you zoom into one of these areas (in this instance, Hobart) you can start to see the pattern resolving, to which you can begin to ask the question: is there a relationship between the proportion of households spending more than 30% of income on mortgage (darker, bluer areas) and the proportion of households spending more than 30% of their income on rent (larger, redder circles)?

Map Visualisations: Choropleth (Custom Ranges)

Introduction

The custom ranges choropleth allows you to specify the values that define the upper and lower limits of the intermediate classes of your “classes” or “bins” that show up on the legend of your map. All of the other classifiers calculate where these breaks lie, based on the different classification method.

The only limitation placed on you is that the lowest limit of the lowest class cannot be lower than the lowest value in your dataset, and the highest limit cannot be higher than then highest value.

Set Up

For this worked example we will look at the distribution of Gini coefficient values across Victoria’s SA2s in 2016.

Once you have added these datasets, you are ready to create your Custom Range Choropleth – click the Inputs tab above to see how to do this

Inputs

Now that you have added your datasets you are ready to create a Custom Range Choropleth map.

To do this, click Maps, Charts & Graphs then Map Visualisations and then Choropleth – Custom Ranges in the Visualisations pop up window (shown below). This will bring up a range of fields that need to be populated. Enter your parameters as you see them below

There is an additional step for these parameters shown in the pop out box. These are explained with the other parameters below

Select a dataset:Here you can choose which of the datasets you would like to display as a map. In this exercise we use ABS – Data by Region – Family & Community (SA2) 2011 – 2016

Select an attribute: This is the field that you want to map. If you want your map to make sense, and actually display the variable you are interested in, it is important to make sure you have selected the right attribute to map together with right classifier. For this example we choose Household Stress: Households with Mortgage Repayments Greater Than or Equal To 30% of Household Income %

Import parameters from:This allows you to bring in the classification parameters from another layer in your session. This is useful if you want to be able to compare two map layers using the same classification.

Number of Classes: This slider allows you to define the number of breaks in your data (minimum of 3, maximum of 12). The number that you choose should depend on the distribution of your values, the number of data points (areas) and the information that you are trying to portray with your data. For this example we choose 5

Class ranges: This will open up the pop up box as shown above, which allows you to edit both the values which specify your legend “bins” or “classes”, as well as the names of those bins. Here we have named them Lowest, Second Lowest, Middle, Second Highest, and Highest. We’ve also specified the values of the bins to be every 0.05 between our lowest (0.247) and our highest (0.419) Gini coefficent values.

Select a palette type:Here you can choose the type of colour scheme for your data – Sequential, which shifts from a shade of one colour to another; Qualitative, where the colours are unique along the palette (used for Pre-classified) ; and Diverging, where colours shift to two colours from a central point along a natural spectrum. For this instance we choose Sequential.

Palette:This allows you to choose the actual colours of your palette (you can switch the ends of the palette around by clicking the Reverse Palette box at the bottom of the box. AURIN uses colours generated by Colour Brewer. For this example we select Greens.

Hover Opacity:This slider allows you to define how opaque you want specific areas to be when you hover over them with a mouse, with the same values as for Default Opacity. Here we select 0.85

Stroke/Line Opacity: This slider allows you to define how opaque your polygon borders will be, with the same values as the Default and Hover Opacity. Here we select 1.0

Name: The default for this field is “[Attribute] -1” It’s a good idea to change the name of this to something that reflects the data, particularly if you plan on having multiple choropleth maps from different datasets. The name that you choose here will also be displayed in the legend automatically generated for your map. Here we use the name: Gini Coefficient

Once you have selected your parameters click Add and Display.

Outputs

Once you click Add and Display on the input box, a map will appear automatically in your viewer which should look something like the map shown below. If you hover over any of the areas, the value for the attribute that you have mapped will show, as well as which group in the legend that specific area belongs to

Hovering over a certain class within the legend will also bring up all of the areas that belong to that specific class (shown below, on a zoomed in version of the above)

Managing and Editing Your Visualisations

Once you have created a visualisation, it will appear in your Visualise panel on the right of your screen (shown below). There are a number of options available to you.

Your visualisations can be turned on and off by clicking either the map or the graph icons to the left of the entry in the panel. For maps, the order that different maps appear in your session depends on the order in which you turn the different ‘layers’ on

You can remove a visualisation by clicking the red cross mark next to the entry. This will be required if you wish to remove the underlying dataset you used to create the visualisation

Clicking on the spanner for a visualisation will bring up the visualisation toolbar (shown below), which will provides you with a few more options

Show/Hide: This option provides the same functionality as the icons to the left of your visualisations

Rename: This allows you to rename your visualisation. This will be important if you plan on building a number of maps and charts in your session

Edit: This option allows you to change the parameters of your visualisation, such as the attributes, the classification system and the transparency

Remove: This option allows you to remove a visualisation from your Visualise panel

Zoom to Extent: This option allows you to zoom to the geographic extent covered by the visualisation

Map Overlays

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